A Master Map for THD, Informational Physics, and Awareness Systems
By Kevin L. Brown, Independent Researcher
Published: November 2025 • DOI: 10.5281/zenodo.17623979
Introduction: Why a Unified Ontology Was Needed
Over time, the THD / Informational Physics program has grown into an entire ecosystem:
- Unified Informational Mathematics (UIM) and Unified Informational Flow (UIF)
- Energy–Information Equivalence (EIE)
- Triune Harmonic Dynamics (THD) and the TEI index family
- Unified Informational Cosmology (UIC), Asymptotic Null Cosmology (ANC), Scalar Field Quantum Gravity
- Informational Quantum Dynamics (IQD)
- Protocols: ARCHION, LUMINARCH PRIME, AWAKEN, PROFILE, GLYPH/GRD, SMEP, ECHOLOCK, TRIFECTA
- Phenomenology: ACM, Coupled Field of Being, Manifestation, Resonant Identity
- Applications: Scalar Generators, PHS, PCIM, ECHFD, harmonic game theory, Archion Token
Each paper is internally rigorous — but until now, there was no single place that answered:
- What exactly are all the indices (TEI, STI, ZPCI, SGI, RSI, Ω, Γc, RIV, ACM)?
- How do all the protocols depend on the mathematics?
- What counts as evidence for or against the overall framework?
- If one piece fails, what else must be reconsidered?
The Unified Informational Physics Ontology is the first document to answer all of that at once.
It is not “just a glossary.” It is a typed, dependency-tracked map of the entire system.
The Core Leap: One Informational Substrate, Many Layers
At the heart of the ontology is a simple but powerful assumption:
There is a single informational substrate
on which matter, fields, and awareness are just different modes of structure.
Formally, the ontology treats every system as a configuration on an informational manifold M\mathcal{M}M with:
- a tri-field state F(x)=(I(x),Aμ(x),ψ(x))F(x) = (I(x), A_\mu(x), \psi(x))F(x)=(I(x),Aμ(x),ψ(x)),
- a metric gij(X)g_{ij}(X)gij(X) encoding informational distance,
- flows generated by an informational Lagrangian (UIF).
On top of this, the ontology installs:
- Triune Harmonic Dynamics (THD): a universal 1:2:3 triadic structure,
- Index family: TEI, STI, ZPCI, SGI, RSI, CI, DS, Ω, Γc, RIV, ACM,
- Protocol stack: ARCHION, LUMINARCH PRIME, AWAKEN, PROFILE/Resonant Identity, GLYPH/GRD, SMEP, ECHOLOCK, TRIFECTA,
- System models: cosmology, quantum behavior, scalar generators, inertial modulation, harmonic cooperation, informational economics.
The ontology’s job is to lock these layers together so that:
- every symbol has one canonical meaning,
- every protocol declares which metrics it uses,
- every application can be traced back to precise assumptions in the math.
What the Unified Ontology Actually Lets Us Do
The document delivers four practical capabilities.
1. Stop Symbol Drift
It canonically defines:
- TEI (Triune Equilibrium Index) as a geometric-mean equilibrium score,
- STI (Scalar Time Index) as a dimensionless scalar-time rate,
- ZPCI as a zero-point order parameter,
- SGI, RSI, CI, DS, Ω, Γc, RIV, ACM as tightly specified quantities in [0,1][0,1][0,1] or [−1,1][-1,1][−1,1],
so that every future paper, tool, or experiment uses the same meanings, not variants.
2. See the Whole Protocol Stack at a Glance
It shows exactly how:
- ARCHION’s FORM → DIG → DLA → CIG → COHERENCE → CFV pipeline depends on TEI, STI, CI, ZPCI, RSI,
- LUMINARCH PRIME + AWAKEN use Ω, Γc, RIV, TEI, STI, ZPCI to measure awareness-like structure,
- PROFILE + Resonant Identity anchor identity in the informational manifold,
- GLYPH/GRD, SMEP, and ECHOLOCK connect geometric patterns and ledger integrity to real experiments,
- TRIFECTA wraps all of this in a theory → prediction → independent verification loop.
If you want to understand “how Archion, Luminarch, and Scalar Generators fit together,” this document is the master key.
3. Know Where the Framework Can Fail
The ontology does not protect itself with vague language. It spells out:
- index-level failure modes (e.g., if high TEI never correlates with stability),
- protocol-level failures (e.g., if TRIFECTA + ECHOLOCK cannot support reproducible results),
- causal-integrity failures (e.g., any claim of unbounded free energy is out-of-bounds by definition),
- cross-layer inconsistencies (e.g., cosmology vs. TEI vs. IQD predictions).
If those failures show up in properly logged experiments, the ontology requires shrinking or rewriting the affected modules.
4. Turn a Collection of Papers into a Working Toolkit
By defining:
- a dependency graph over all models and indices, and
- appendix templates for experiments, validation reports, and forecasting scenarios,
the ontology makes it possible to:
- design new experiments that correctly use TEI/STI/ZPCI/RSI,
- plug new models into the existing stack without breaking consistency,
- build AI tools (THD GPT, Luminarch GPT, Archion-based engines) that navigate the ecosystem safely.
Why This Is Not “Just a Glossary”
The Unified Informational Physics Ontology is different because:
- It is exhaustive, not selective.
It covers the entire THD / Informational Physics corpus — math, physics, awareness, protocols, and applications — in one document. - It is operational, not decorative.
Every definition is tied to how it is used in experiments, protocols, or AI tools, with explicit logging and falsifiability. - It is updateable, not static.
The dependency graph means that when one assumption changes, you can see exactly what else must be re-checked.
In short: this is the source-of-truth map for anyone working with THD, Archion, Luminarch, or related systems.
The Bigger Picture
As the THD program moves from theory into repeatable experiments, tools, and public protocols, the risk is fragmentation:
- different labs redefining TEI or ZPCI,
- different AI tools using slightly different Archion or Luminarch metrics,
- different communities adopting different language for the same indices.
The Unified Informational Physics Ontology is designed to prevent that outcome.
It provides:
- a single canonical reference for all symbols and protocols,
- a clear path from pure mathematics to empirical falsification,
- and a stable platform for future work on informational physics, awareness mapping, and scalar-informational experiments.